CN112036607A - Wind power output fluctuation prediction method and device based on output level and storage medium - Google Patents
Wind power output fluctuation prediction method and device based on output level and storage medium Download PDFInfo
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Abstract
The invention discloses a wind power output fluctuation prediction method based on an output level, which comprises the following steps: dividing the wind power output sequence to obtain a plurality of divided regions; splitting each data set according to the wind power output level to obtain data sample sets under different wind power output levels; establishing a corresponding relation between each wind power output level and a wind power fluctuation interval of a future time period, and carrying out probability statistics and fitting on the wind power fluctuation amount of the future time period corresponding to the wind power output level to obtain a wind power fluctuation probability model; and carrying out the fluctuation quantity statistics of the confidence coefficient on the wind power fluctuation probability model of each wind power output level under each prediction step length to obtain the fluctuation interval prediction condition of each step length under each cluster. The invention also discloses a wind power output fluctuation prediction device based on the output level and a storage medium.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a wind power output fluctuation prediction method and device based on an output level and a storage medium.
Background
With the gradual depletion of global fossil resource reserves and the gradual deepening of low carbon and environmental protection concepts in recent years, the development and utilization of renewable energy sources represented by wind energy are increasingly regarded by the international society. Compared with the traditional energy, the wind power generation is greatly influenced by climate, and the output of the wind power generation has the characteristics of volatility, intermittence, randomness and the like, so that the large-scale wind power integration can bring huge challenges to the safe operation and the actual scheduling work of a power system. In order to improve the wind power grid-connected capacity, accurate and reliable wind power prediction is essential for power system scheduling.
In the process of implementing the method, the technical personnel in the field find that the deterministic wind power point prediction in the prior art still has a large error, and the prediction result of the deterministic wind power point prediction cannot reflect the wind power fluctuation characteristic. Therefore, a wind power output fluctuation interval prediction method capable of improving prediction accuracy is needed.
Disclosure of Invention
The embodiment of the invention provides a wind power output fluctuation prediction method based on the output level, which can improve the accuracy of wind power output fluctuation prediction based on the output level and accurately reflect the wind power fluctuation characteristics.
The embodiment of the invention provides a wind power output fluctuation prediction method based on an output level, which comprises the following steps:
dividing the wind power output sequence to obtain a plurality of divided regions;
splitting each data set according to the wind power output level to obtain data sample sets under different wind power output levels;
establishing a corresponding relation between each wind power output level and a wind power fluctuation interval of a future time period, and carrying out probability statistics and fitting on the wind power fluctuation amount of the future time period corresponding to the wind power output level to obtain a wind power fluctuation probability model;
and carrying out the fluctuation quantity statistics of the confidence coefficient on the wind power fluctuation probability model of each wind power output level under each prediction step length to obtain the fluctuation interval prediction condition of each step length under each cluster.
As an improvement of the above scheme, the dividing the wind power output sequence to obtain a plurality of divided regions specifically includes:
dividing the wind power output sequence to obtain a plurality of first divided areas; dividing the first division area to obtain a plurality of second division areas;
wherein each first division interval is 2160 hours; each of the second divided areas was 2 hours.
As an improvement of the above scheme, the splitting is performed on each data set according to the wind power output level to obtain data sample sets under different wind power output levels, and the method specifically includes:
and further splitting each data set into data sample sets under different output levels according to the output levels based on a sample sharing principle.
As an improvement of the above scheme, the establishing of the corresponding relationship between each wind power output level and the wind power fluctuation interval of the future time period, and performing probability statistics and fitting on the wind power fluctuation amount of the future time period corresponding to the wind power output level to obtain a wind power fluctuation probability model specifically include:
establishing corresponding relations between the wind power output levels and wind power fluctuation intervals of 30 minutes, 1 hour and 2 hours in the future;
carrying out probability statistics on the wind power fluctuation amount of 30 minutes, 1 hour and 2 hours in the future under different wind power output levels, fitting the probability distribution of the fluctuation amount by adopting a t Location-Scale distribution function, and establishing a wind power fluctuation probability model under each prediction step length under different wind power output levels.
As an improvement of the above scheme, the statistics of the fluctuation amount of the confidence coefficient is performed on the wind power fluctuation probability model of each wind power output level under each prediction step length to obtain the fluctuation interval prediction condition of each step length under each cluster, specifically including:
carrying out probability statistics on the wind power fluctuation amount of 30 minutes, 1 hour and 2 hours in the future under different wind power output levels, fitting the probability distribution of the fluctuation amount by adopting a t Location-Scale distribution function, and establishing a wind power fluctuation probability model under each prediction step length under different wind power output levels.
And counting the fluctuation amounts of the t Location-Scale probability model at each prediction step length under different wind power output levels at 95% and 90% confidence degrees to obtain a fluctuation interval prediction table of each step length under different wind power output levels.
As an improvement of the above scheme, the method further comprises the following steps: roughly predicting the output of each historical output data point on the premise of assuming that the output variation trend is unchanged;
assuming that the output variation trend is unchanged, wind power P at historical time t-delta t is usedt-ΔtAnd wind power P at current moment ttEstimating the wind power output at the time of t + delta t, specifically as follows:
in the formula (I), the compound is shown in the specification,
and predicting the output of the wind power at the time of t + delta t.
As an improvement of the above scheme, the method further comprises the following steps: wind power output prediction method according to t + delta t moment
Selecting a corresponding interval where the wind power output level is located according to the fluctuation interval prediction table;
obtaining a fluctuation quantity prediction interval under the corresponding delta t step length according to the corresponding interval
As an improvement of the above scheme, the method further comprises the following steps:
superposing the fluctuation quantity prediction interval to wind power prediction power at t + delta t moment
In the above, the wind power output interval prediction result after the future delta t time is obtained
The calculation formula of the prediction interval is as follows:
the embodiment of the invention correspondingly provides a wind power output fluctuation prediction device based on an output level, which comprises the following steps: the wind power output fluctuation prediction method based on the output level comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to realize the wind power output fluctuation prediction method based on the output level.
The third embodiment of the present invention correspondingly provides a computer-readable storage medium, which is characterized in that the computer-readable storage medium includes a stored computer program, and when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the wind power output fluctuation prediction method based on the output level according to the first embodiment of the present invention.
The wind power output fluctuation prediction method based on the output level provided by the embodiment of the invention has the following beneficial effects:
according to different seasons, historical wind power time sequence output data of 2 hours are used as analysis samples, and historical 2-hour wind power output is divided into 2 short-time output levels on the basis of sample sharing; and obtaining wind power output fluctuation interval prediction tables corresponding to different prediction step lengths of 30 minutes, 1 hour, 2 hours and the like under different output levels by establishing corresponding relations between the short-time output levels of each wind power and wind power fluctuation intervals of 30 minutes, 1 hour and 2 hours in the future. By assuming that the output variation trend is unchanged, estimating wind power output values under different prediction step lengths of 30 minutes, 1 hour, 2 hours and the like, and by analyzing the output level of the output value, predicting wind power fluctuation intervals and output intervals under different time scales in the future, important guidance and reference are provided for the daily actual scheduling of the power system; the method is beneficial for the dispatcher to better know the possible uncertainty of future change and carry out risk assessment so as to ensure the economical and safe operation of the system; the wind power output fluctuation prediction accuracy based on the output level is improved, and the wind power fluctuation characteristics are accurately reflected.
Drawings
Fig. 1 is a schematic flow chart of a wind power output fluctuation prediction method based on an output level according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of confidence probability distribution of historical 2-hour wind power output level according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a wind power fluctuation model based on t Location-Scale distribution according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of fluctuation intervals with different confidence levels for each prediction step according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of the output interval prediction according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a schematic flow chart of a wind power output fluctuation prediction method based on an output level according to an embodiment of the present invention is shown, including:
s101, dividing a wind power output sequence to obtain a plurality of divided regions;
further, the wind power output sequence is divided to obtain a plurality of divided regions, and the method specifically comprises the following steps:
dividing the wind power output sequence to obtain a plurality of first divided areas; dividing the first division area to obtain a plurality of second division areas;
wherein each first division interval is 2160 hours; each second division was performed for 2 hours.
S102, splitting each data set according to the wind power output level to obtain data sample sets under different wind power output levels;
further, each data set is split according to the wind power output level, so that data sample sets under different wind power output levels are obtained, and the method specifically comprises the following steps:
and further splitting each data set into data sample sets under different output levels according to the output levels based on a sample sharing principle.
Specifically, according to a sample sharing principle, each data set is further split into two data sample sets under large and small output levels according to the output level. The wind power output is divided into 2 sample subsets by establishing a wind power output confidence probability distribution diagram (wherein the abscissa is the wind power size, and the ordinate is the confidence probability), selecting points with the confidence probability of 50%, and the abscissa is a dividing point for equally dividing sample data into large and small output intervals, as shown in fig. 2.
S103, establishing a corresponding relation between each wind power output level and a wind power fluctuation interval of a future time period, and carrying out probability statistics and fitting on wind power fluctuation quantity of the future time period corresponding to the wind power output level to obtain a wind power fluctuation probability model;
specifically, referring to fig. 3, a schematic diagram of a wind power fluctuation probability model under each prediction step length under different wind power short-time output levels is shown.
Further, establishing a corresponding relation between each wind power output level and a wind power fluctuation interval of a future time period, and carrying out probability statistics and fitting on the wind power fluctuation amount of the future time period corresponding to the wind power output level to obtain a wind power fluctuation probability model, which specifically comprises the following steps:
establishing corresponding relations between the wind power output levels and wind power fluctuation intervals of 30 minutes, 1 hour and 2 hours in the future;
carrying out probability statistics on the wind power fluctuation amount of 30 minutes, 1 hour and 2 hours in the future under different wind power output levels, fitting the probability distribution of the fluctuation amount by adopting a t Location-Scale distribution function, and establishing a wind power fluctuation probability model under each prediction step length under different wind power output levels.
S104, carrying out confidence fluctuation quantity statistics on the wind power fluctuation probability model of each wind power output level under each prediction step length to obtain the fluctuation interval prediction condition of each step length under each cluster;
further, the wind power fluctuation probability model of each wind power output level under each prediction step is subjected to fluctuation quantity statistics of confidence coefficient to obtain the fluctuation interval prediction condition of each step under each cluster, and the method specifically comprises the following steps:
carrying out probability statistics on the wind power fluctuation amount of 30 minutes, 1 hour and 2 hours in the future under different wind power output levels, fitting the probability distribution of the fluctuation amount by adopting a t Location-Scale distribution function, and establishing a wind power fluctuation probability model under each prediction step length under different wind power output levels.
And counting the fluctuation amounts of the t Location-Scale probability model at each prediction step length under different wind power output levels at 95% and 90% confidence degrees to obtain a fluctuation interval prediction table of each step length under different wind power output levels.
Specifically, see fig. 4, which is a schematic diagram of fluctuation intervals of different confidence levels for each prediction step.
Further, still include: roughly predicting the output of each historical output data point on the premise of assuming that the output variation trend is unchanged;
assuming that the output variation trend is unchanged, wind power P at historical time t-delta t is usedt-ΔtAnd wind power P at current moment ttEstimating the wind power output at the time of t + delta t, specifically as follows:
in the formula (I), the compound is shown in the specification,
and predicting the output of the wind power at the time of t + delta t.
Further, still include: wind power output prediction method according to t + delta t moment
Selecting a corresponding interval where the wind power output level is located according to the fluctuation interval prediction table;
obtaining a fluctuation amount prediction interval under the corresponding delta t step length according to the corresponding interval
Further, referring to fig. 5, the method further includes:
wind power prediction power obtained by superposing fluctuation quantity prediction interval to t + delta t moment
In the above, the wind power output interval prediction result after the future delta t time is obtained
The calculation formula of the prediction interval is as follows：
The wind power output fluctuation prediction method, the wind power output fluctuation prediction device and the storage medium based on the output level have the following beneficial effects that:
according to different seasons, historical wind power time sequence output data of 2 hours are used as analysis samples, and historical 2-hour wind power output is divided into 2 short-time output levels on the basis of sample sharing; and obtaining wind power output fluctuation interval prediction tables corresponding to different prediction step lengths of 30 minutes, 1 hour, 2 hours and the like under different output levels by establishing corresponding relations between the short-time output levels of each wind power and wind power fluctuation intervals of 30 minutes, 1 hour and 2 hours in the future. By assuming that the output variation trend is unchanged, estimating wind power output values under different prediction step lengths of 30 minutes, 1 hour, 2 hours and the like, and by analyzing the output level of the output value, predicting wind power fluctuation intervals and output intervals under different time scales in the future, important guidance and reference are provided for the daily actual scheduling of the power system; the method is beneficial for the dispatcher to better know the possible uncertainty of future change and carry out risk assessment so as to ensure the economical and safe operation of the system; the wind power output fluctuation prediction accuracy based on the output level is improved, and the wind power fluctuation characteristics are accurately reflected.
Correspondingly, the embodiment of the invention provides a wind power output fluctuation prediction device based on the output level, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the wind power output fluctuation prediction method based on the output level when executing the computer program. The wind power output fluctuation prediction device based on the output level can be computing equipment such as a desktop computer, a notebook computer, a palm computer and a cloud server. The wind power output fluctuation prediction device based on the output level can comprise, but is not limited to, a processor and a memory.
The third embodiment of the present invention correspondingly provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, where when the computer program runs, a device in which the computer-readable storage medium is located is controlled to execute the wind power output fluctuation prediction method based on the output level according to the first embodiment of the present invention.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. The general processor can be a microprocessor or the processor can also be any conventional processor and the like, the processor is a control center of the wind power output fluctuation prediction device based on the output level, and various interfaces and lines are utilized to connect all parts of the whole wind power output fluctuation prediction device based on the output level.
The memory can be used for storing the computer program and/or the module, and the processor realizes various functions of the wind power output fluctuation prediction device based on the output level by running or executing the computer program and/or the module stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The wind power output fluctuation prediction device integrated module/unit based on the output level can be stored in a computer readable storage medium if the module/unit is realized in the form of a software functional unit and sold or used as an independent product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A wind power output fluctuation prediction method based on output level is characterized by comprising the following steps:
dividing the wind power output sequence to obtain a plurality of divided regions;
splitting each data set according to the wind power output level to obtain data sample sets under different wind power output levels;
establishing a corresponding relation between each wind power output level and a wind power fluctuation interval of a future time period, and carrying out probability statistics and fitting on the wind power fluctuation amount of the future time period corresponding to the wind power output level to obtain a wind power fluctuation probability model;
and carrying out the fluctuation quantity statistics of the confidence coefficient on the wind power fluctuation probability model of each wind power output level under each prediction step length to obtain the fluctuation interval prediction condition of each step length under each cluster.
2. The output level-based wind power output fluctuation prediction method of claim 1, wherein the dividing of the wind power output sequence into a plurality of divided regions specifically comprises:
dividing the wind power output sequence to obtain a plurality of first divided areas; dividing the first division area to obtain a plurality of second division areas;
wherein each first division interval is 2160 hours; each of the second divided areas was 2 hours.
3. The output level-based wind power output fluctuation prediction method of claim 2, wherein each data set is split according to the wind power output level to obtain data sample sets at different wind power output levels, and the method specifically comprises:
and further splitting each data set into data sample sets under different output levels according to the output levels based on a sample sharing principle.
4. The output level-based wind power output fluctuation prediction method according to claim 3, wherein the establishing of the corresponding relationship between each wind power output level and the wind power fluctuation interval of the future time period, and the performing of probability statistics and fitting on the wind power fluctuation amount of the future time period corresponding to the wind power output level to obtain the wind power fluctuation probability model specifically comprise:
establishing corresponding relations between the wind power output levels and wind power fluctuation intervals of 30 minutes, 1 hour and 2 hours in the future;
carrying out probability statistics on the wind power fluctuation amount of 30 minutes, 1 hour and 2 hours in the future under different wind power output levels, fitting the probability distribution of the fluctuation amount by adopting a t Location-Scale distribution function, and establishing a wind power fluctuation probability model under each prediction step length under different wind power output levels.
5. The wind power output fluctuation prediction method based on the output level of claim 4, wherein the fluctuation amount statistics of the confidence coefficient is performed on the wind power fluctuation probability model of each wind power output level in each prediction step length to obtain the fluctuation interval prediction condition of each step length in each cluster, specifically comprising:
carrying out probability statistics on the wind power fluctuation amount of 30 minutes, 1 hour and 2 hours in the future under different wind power output levels, fitting the probability distribution of the fluctuation amount by adopting a t Location-Scale distribution function, and establishing a wind power fluctuation probability model under each prediction step length under different wind power output levels.
And counting the fluctuation amounts of the t Location-Scale probability model at each prediction step length under different wind power output levels at 95% and 90% confidence degrees to obtain a fluctuation interval prediction table of each step length under different wind power output levels.
6. The wind power output fluctuation prediction method based on the output level of claim 5, further comprising: roughly predicting the output of each historical output data point on the premise of assuming that the output variation trend is unchanged;
assuming that the output variation trend is unchanged, wind power P at historical time t-delta t is usedt-ΔtAnd wind power P at current moment ttEstimating the wind power output at the time of t + delta t, specifically as follows:
in the formula (I), the compound is shown in the specification,
and predicting the output of the wind power at the time of t + delta t.
7. The wind power output fluctuation prediction method based on output level of claim 6, further comprising: wind power output prediction method according to t + delta t moment
Selecting a corresponding interval where the wind power output level is located according to the fluctuation interval prediction table;
obtaining a fluctuation quantity prediction interval under the corresponding delta t step length according to the corresponding interval
8. The method of claim 7, further comprising:
superposing the fluctuation quantity prediction interval to wind power prediction power at t + delta t moment
In the above, the wind power output interval prediction result after the future delta t time is obtained
The calculation formula of the prediction interval is as follows:
9. a wind power output fluctuation prediction apparatus based on output level, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing a wind power output fluctuation prediction method based on output level according to any one of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform a method for predicting wind power output fluctuation based on output level according to any one of claims 1 to 8.
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| CN117332901A (en) * | 2023-10-17 | 2024-01-02 | 南方电网数字电网研究院有限公司 | New energy small time scale power prediction method adopting layered time aggregation strategy |
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